## [1] "R version 4.5.1 (2025-06-13)"
## [1] "tidyverse version 2.0.0"
## [1] "knitr version 1.50"
## [1] "ggpubr version 0.6.0"
## [1] "ggrain version 0.0.4"
## [1] "Hmisc version 5.2.3"
## [1] "rstatix version 0.7.2"
## [1] "emmeans version 1.11.1"
## [1] "flextable version 0.9.9"
## [1] "officer version 0.6.10"
## [1] "english version 1.2.6"
Inclusion criteria applying to all participants:
Additional inclusion criteria for ASD participants:
Additional inclusion criteria for BPD participants:
Additional inclusion criteria for comparison participants:
Furthermore, we applied the following exclusion criteria:
Testing was discontinued if participants withdrew their consent, exclusion criteria applied or in the case of technical difficulties.
We collected the following self-report questionnaires:
For all participants recruited for this project, we also collected the short version of the Borderline Symptom List, BSL-237.
For 37 of the dyads, we used a plexiglass screen placed between the interaction partners on the table to decrease the chances of spreading an undetected infection. We applied a transparent anti-reflection foil to reduce any mirroring effects. During the conversation, participants took off their face masks. We asked these participants how much the plexiglass influenced them during the interactions (plexi; scale from 0 to 3). All participants were asked how much the cameras influenced their behaviour (video; scale from 0 to 3). Rapport is a sum of the following ratings, all on scales from 0 to 3, thus ranging from 0 to 15:
| measurement | ASD | BPD | COMP | BPDvsASD | COMPvsASD | COMPvsBPD |
|---|---|---|---|---|---|---|
| ADC_total | 49.06 (±17.17), n = 17 | 76.95 (±14.98), n = 21 | 34.99 (±23.86), n = 82 | 0.006* | 0.243 | 0.000* |
| AQ_total | 34.29 (±6.18), n = 17 | 23.67 (±4.72), n = 21 | 14.57 (±5.27), n = 82 | 0.000* | 0.000* | 0.000* |
| BDI_total | 15.94 (±11.57), n = 17 | 24.81 (±10.48), n = 21 | 3.99 (±3.79), n = 82 | 0.715 | 0.000* | 0.000* |
| BERT.acc | 0.80 (±0.07), n = 17 | 0.82 (±0.09), n = 21 | 0.83 (±0.07), n = 82 | 1.000 | 0.919 | 1.000 |
| BERT.rt | 5.79 (±2.78), n = 17 | 3.84 (±1.24), n = 21 | 3.33 (±1.33), n = 82 | 0.453 | 0.000* | 0.637 |
| BSL_total | NaN (±NA), n = 0 | 46.71 (±21.44), n = 21 | 7.19 (±6.84), n = 37 | NA | NA | 0.000* |
| IQ.estimate | 116.47 (±13.90), n = 17 | 109.60 (±9.54), n = 20 | 112.77 (±13.26), n = 82 | 1.000 | 1.000 | 1.000 |
| SMS_total | 5.47 (±2.81), n = 17 | 11.67 (±2.11), n = 21 | 9.80 (±2.62), n = 82 | 0.000* | 0.000* | 0.027* |
| SPF_total | 37.00 (±7.20), n = 17 | 40.95 (±8.36), n = 21 | 44.73 (±5.82), n = 82 | 1.000 | 0.000* | 0.328 |
| TAS_total | 61.76 (±11.03), n = 17 | 55.33 (±11.60), n = 21 | 37.55 (±8.53), n = 82 | 1.000 | 0.000* | 0.000* |
| age | 37.59 (±13.19), n = 17 | 28.38 (±10.02), n = 21 | 28.89 (±10.18), n = 82 | 0.506 | 0.243 | 1.000 |
| plexi | 0.82 (±0.53), n = 17 | 0.85 (±0.90), n = 13 | 0.86 (±0.60), n = 43 | 1.000 | 1.000 | 1.000 |
| rapport | 12.24 (±2.19), n = 17 | 11.95 (±2.64), n = 21 | 12.33 (±2.39), n = 81 | 1.000 | 1.000 | 1.000 |
| video | 0.71 (±0.69), n = 17 | 0.81 (±0.75), n = 21 | 0.63 (±0.68), n = 82 | 1.000 | 1.000 | 1.000 |
## ilabel
## gender ASD BPD COMP
## fem 6 14 52
## mal 11 7 30
##
## Pearson's Chi-squared test
##
## data: tb.gen
## X-squared = 5.1108, df = 2, p-value = 0.07766
We only included data of participants with a mean confidence of tracked frames greater than 75% and more than 90% successfully tracked frames. Facial expressions were captured as action units. We did not extract emotional expressions from these facial expressions as coherence between facial expressions and emotions is not a given and might be even less so for autistic people8.
For the calculation of synchronisation, we included rotational parameters (yaw, roll, pitch) as well as the same action units as in our previous study9:
We also extracted total facial expressiveness as mean intensity of all action units for each interaction partner to be included in the MovEx and the CROSSturn models. For the CROSSturn model, we also included other action units, as listed below.
These correspond to the following movements:
Furthermore, we used translational head position parameters to infer head motion using the following formula with \(\Delta_t\) referring to the respective frame-to-frame changes:
\[\text{head movement} = \sqrt{\Delta_t x^2 + \Delta_t y^2 + \Delta_t z^2}\]
This figure shows body (red and purple) and head (yellow and orange) regions of interests of each interaction partner separately:
There was always a space between the head and body region. Regions were chosen such that they cover the full range of motion throughout one conversation of one interaction partner. Thus, their sizes differed which is why we scaled all values.
In addition to using the motion quantity to compute synchronisation, we also extracted total movement in each region of interest for each interaction partner to be included in the MovEx model.
We extracted pitch using praat’s autocorrelation method, a technique widely recognized for its reliability and accuracy10. We implemented a two-step pitch extraction method, as outlined by Hirst11. First, to capture a broad range of frequencies, we set a low pitch floor of 50 Hz and a high pitch ceiling of 700 Hz, with a time step of 15 ms. All other parameters were set to Praat’s default values. Second, using these initial pitch values, we determined the first and third quartiles of pitch for each participant and task. We then used these quartiles to compute individual pitch floors and ceilings with the following algorithm:
\[\text{floor} = \min\left( 0.75 \cdot Q_{1, hobbies}, 0.75 \cdot Q_{1, mealplanning}\right)\]
\[\text{ceiling} = \max\left( 2.5 \cdot Q_{3, hobbies}, 2.5 \cdot Q_{3, mealplanning}\right)\]
We then used these individual pitch floors (range = 46 to 168Hz, mean = 109.7 ± 31.8) and ceilings (range = 250 to 839Hz, mean = 481.7 ± 147.4) to extract pitch. To ensure an equal number of frames for all participants, we maintained a consistent time step across all analyses. By default, praat calculates this time step using the following formula:
\[\text{timestep} = \frac{0.75}{\text{floor}}\]
Here, we used the same time step of 0.016 as in our previous study12 which was determined based on the minimum individual pitch floor of that sample. Since the new sample did not include anyone with a lower pitch floor, the time step fits both samples.
Intensity was extracted by convolving the squared sound with a Gaussian analysis window. We used praat’s default values of minimum pitch 100Hz and time step of 0.01s.
To estimate synchrony, we extracted continuous pitch and intensity time series for every millisecond of the recording. For pitch extraction, we used consistent parameters across all participants instead of individualized settings. This was necessary because the analysis width depends on the pitch floor. Given the heterogeneity of our sample, we opted for a wide range of considered frequencies, setting the pitch floor at 50Hz and the pitch ceiling at 700Hz. For intensity, we relied on Praat’s default values.
In the case of turn-based synchronization, we correlated the median pitch or intensity of each turn with the median pitch or intensity of the preceding turn.
Next, we used the uhm-o-meter13,14 to differentiate between periods of speaking and silence, identify syllables and extract several prosodic features (total number of syllables, total number of silent phases, duration of speaking as phonation time, speech rate as number of syllables per second, articulation rate as number of syllables per phonation time, average syllable duration and silence-to-turn ratio). The resulting speaking and silent instances were visually and aurally inspected to verify the accuracy of the algorithm.
We captured two types of cross-modal features:
We used the following settings for our windowed lagged cross-correlation (WLCC):
| measure | window | step | lag |
|---|---|---|---|
| Facial action units synchronisation | 7 | 4 | 2 |
| Body MEA synchronisation | 30 | 15 | 5 |
| Head MEA synchronisation | 30 | 15 | 5 |
| Intrapersonal synchrony | 30 | 15 | 5 |
| Pitch synchrony | 16 | 8 | 2 |
| Intensity synchrony | 16 | 8 | 2 |
For each window, the maximum correlation value was chosen out of all relevant lags (peack-picking). We cross-correlated head movements (from OpenFace) with body motion energy time series (from MEA) to estimate intrapersonal synchrony.
This list shows all features without the information of conversation, i.e., each of these features was added twice to the model, once from the mealplanning and once from the hobbies conversation. The number of features per model is displayed as well. For many of the extracted features, we calculated summary scores some which are indicated by abbreviations (mean, md = median, sd = standard deviation, min = minimum, max = maximum, ske = skewness and kurtosis)
| model | features | no of features |
|---|---|---|
| BODYsync | min_M_bodysync, max_M_bodysync, sd_M_bodysync, mean_M_bodysync, md_M_bodysync, skew_M_bodysync, kurtosis_M_bodysync | 14 |
| CROSSsync | min_M_LOF, min_M_ROF, max_M_LOF, max_M_ROF, md_M_LOF, md_M_ROF, mean_M_LOF, mean_M_ROF, sd_M_LOF, sd_M_ROF, kurtosis_M_LOF, kurtosis_M_ROF, skew_M_LOF, skew_M_ROF | 28 |
| CROSSturn | self_min_M_AU01_r, self_min_M_AU02_r, self_min_M_AU04_r, self_min_M_AU05_r, self_min_M_AU06_r, self_min_M_AU07_r, self_min_M_AU09_r, self_min_M_AU10_r, self_min_M_AU12_r, self_min_M_AU14_r, self_min_M_AU15_r, self_min_M_AU17_r, self_min_M_AU20_r, self_min_M_AU23_r, self_min_M_AU25_r, self_min_M_AU26_r, self_min_M_AU45_r, self_min_M_MEA_body, self_min_M_MEA_head, self_max_M_AU01_r, self_max_M_AU02_r, self_max_M_AU04_r, self_max_M_AU05_r, self_max_M_AU06_r, self_max_M_AU07_r, self_max_M_AU09_r, self_max_M_AU10_r, self_max_M_AU12_r, self_max_M_AU14_r, self_max_M_AU15_r, self_max_M_AU17_r, self_max_M_AU20_r, self_max_M_AU23_r, self_max_M_AU25_r, self_max_M_AU26_r, self_max_M_AU45_r, self_max_M_MEA_body, self_max_M_MEA_head, self_md_M_AU01_r, self_md_M_AU02_r, self_md_M_AU04_r, self_md_M_AU05_r, self_md_M_AU06_r, self_md_M_AU07_r, self_md_M_AU09_r, self_md_M_AU10_r, self_md_M_AU12_r, self_md_M_AU14_r, self_md_M_AU15_r, self_md_M_AU17_r, self_md_M_AU20_r, self_md_M_AU23_r, self_md_M_AU25_r, self_md_M_AU26_r, self_md_M_AU45_r, self_md_M_MEA_body, self_md_M_MEA_head, self_mean_M_AU01_r, self_mean_M_AU02_r, self_mean_M_AU04_r, self_mean_M_AU05_r, self_mean_M_AU06_r, self_mean_M_AU07_r, self_mean_M_AU09_r, self_mean_M_AU10_r, self_mean_M_AU12_r, self_mean_M_AU14_r, self_mean_M_AU15_r, self_mean_M_AU17_r, self_mean_M_AU20_r, self_mean_M_AU23_r, self_mean_M_AU25_r, self_mean_M_AU26_r, self_mean_M_AU45_r, self_mean_M_MEA_body, self_mean_M_MEA_head, self_sd_M_AU01_r, self_sd_M_AU02_r, self_sd_M_AU04_r, self_sd_M_AU05_r, self_sd_M_AU06_r, self_sd_M_AU07_r, self_sd_M_AU09_r, self_sd_M_AU10_r, self_sd_M_AU12_r, self_sd_M_AU14_r, self_sd_M_AU15_r, self_sd_M_AU17_r, self_sd_M_AU20_r, self_sd_M_AU23_r, self_sd_M_AU25_r, self_sd_M_AU26_r, self_sd_M_AU45_r, self_sd_M_MEA_body, self_sd_M_MEA_head, self_kurtosis_M_AU01_r, self_kurtosis_M_AU02_r, self_kurtosis_M_AU04_r, self_kurtosis_M_AU05_r, self_kurtosis_M_AU06_r, self_kurtosis_M_AU07_r, self_kurtosis_M_AU09_r, self_kurtosis_M_AU10_r, self_kurtosis_M_AU12_r, self_kurtosis_M_AU14_r, self_kurtosis_M_AU15_r, self_kurtosis_M_AU17_r, self_kurtosis_M_AU20_r, self_kurtosis_M_AU23_r, self_kurtosis_M_AU25_r, self_kurtosis_M_AU26_r, self_kurtosis_M_AU45_r, self_kurtosis_M_MEA_body, self_kurtosis_M_MEA_head, self_skew_M_AU01_r, self_skew_M_AU02_r, self_skew_M_AU04_r, self_skew_M_AU05_r, self_skew_M_AU06_r, self_skew_M_AU07_r, self_skew_M_AU09_r, self_skew_M_AU10_r, self_skew_M_AU12_r, self_skew_M_AU14_r, self_skew_M_AU15_r, self_skew_M_AU17_r, self_skew_M_AU20_r, self_skew_M_AU23_r, self_skew_M_AU25_r, self_skew_M_AU26_r, self_skew_M_AU45_r, self_skew_M_MEA_body, self_skew_M_MEA_head, other_min_M_AU01_r, other_min_M_AU02_r, other_min_M_AU04_r, other_min_M_AU05_r, other_min_M_AU06_r, other_min_M_AU07_r, other_min_M_AU09_r, other_min_M_AU10_r, other_min_M_AU12_r, other_min_M_AU14_r, other_min_M_AU15_r, other_min_M_AU17_r, other_min_M_AU20_r, other_min_M_AU23_r, other_min_M_AU25_r, other_min_M_AU26_r, other_min_M_AU45_r, other_min_M_MEA_body, other_min_M_MEA_head, other_max_M_AU01_r, other_max_M_AU02_r, other_max_M_AU04_r, other_max_M_AU05_r, other_max_M_AU06_r, other_max_M_AU07_r, other_max_M_AU09_r, other_max_M_AU10_r, other_max_M_AU12_r, other_max_M_AU14_r, other_max_M_AU15_r, other_max_M_AU17_r, other_max_M_AU20_r, other_max_M_AU23_r, other_max_M_AU25_r, other_max_M_AU26_r, other_max_M_AU45_r, other_max_M_MEA_body, other_max_M_MEA_head, other_md_M_AU01_r, other_md_M_AU02_r, other_md_M_AU04_r, other_md_M_AU05_r, other_md_M_AU06_r, other_md_M_AU07_r, other_md_M_AU09_r, other_md_M_AU10_r, other_md_M_AU12_r, other_md_M_AU14_r, other_md_M_AU15_r, other_md_M_AU17_r, other_md_M_AU20_r, other_md_M_AU23_r, other_md_M_AU25_r, other_md_M_AU26_r, other_md_M_AU45_r, other_md_M_MEA_body, other_md_M_MEA_head, other_mean_M_AU01_r, other_mean_M_AU02_r, other_mean_M_AU04_r, other_mean_M_AU05_r, other_mean_M_AU06_r, other_mean_M_AU07_r, other_mean_M_AU09_r, other_mean_M_AU10_r, other_mean_M_AU12_r, other_mean_M_AU14_r, other_mean_M_AU15_r, other_mean_M_AU17_r, other_mean_M_AU20_r, other_mean_M_AU23_r, other_mean_M_AU25_r, other_mean_M_AU26_r, other_mean_M_AU45_r, other_mean_M_MEA_body, other_mean_M_MEA_head, other_sd_M_AU01_r, other_sd_M_AU02_r, other_sd_M_AU04_r, other_sd_M_AU05_r, other_sd_M_AU06_r, other_sd_M_AU07_r, other_sd_M_AU09_r, other_sd_M_AU10_r, other_sd_M_AU12_r, other_sd_M_AU14_r, other_sd_M_AU15_r, other_sd_M_AU17_r, other_sd_M_AU20_r, other_sd_M_AU23_r, other_sd_M_AU25_r, other_sd_M_AU26_r, other_sd_M_AU45_r, other_sd_M_MEA_body, other_sd_M_MEA_head, other_kurtosis_M_AU01_r, other_kurtosis_M_AU02_r, other_kurtosis_M_AU04_r, other_kurtosis_M_AU05_r, other_kurtosis_M_AU06_r, other_kurtosis_M_AU07_r, other_kurtosis_M_AU09_r, other_kurtosis_M_AU10_r, other_kurtosis_M_AU12_r, other_kurtosis_M_AU14_r, other_kurtosis_M_AU15_r, other_kurtosis_M_AU17_r, other_kurtosis_M_AU20_r, other_kurtosis_M_AU23_r, other_kurtosis_M_AU25_r, other_kurtosis_M_AU26_r, other_kurtosis_M_AU45_r, other_kurtosis_M_MEA_body, other_kurtosis_M_MEA_head, other_skew_M_AU01_r, other_skew_M_AU02_r, other_skew_M_AU04_r, other_skew_M_AU05_r, other_skew_M_AU06_r, other_skew_M_AU07_r, other_skew_M_AU09_r, other_skew_M_AU10_r, other_skew_M_AU12_r, other_skew_M_AU14_r, other_skew_M_AU15_r, other_skew_M_AU17_r, other_skew_M_AU20_r, other_skew_M_AU23_r, other_skew_M_AU25_r, other_skew_M_AU26_r, other_skew_M_AU45_r, other_skew_M_MEA_body, other_skew_M_MEA_head | 532 |
| FACEsync | min_M_AU01_r, max_M_AU01_r, sd_M_AU01_r, mean_M_AU01_r, md_M_AU01_r, skew_M_AU01_r, kurtosis_M_AU01_r, min_M_AU02_r, max_M_AU02_r, sd_M_AU02_r, mean_M_AU02_r, md_M_AU02_r, skew_M_AU02_r, kurtosis_M_AU02_r, min_M_AU06_r, max_M_AU06_r, sd_M_AU06_r, mean_M_AU06_r, md_M_AU06_r, skew_M_AU06_r, kurtosis_M_AU06_r, min_M_AU07_r, max_M_AU07_r, sd_M_AU07_r, mean_M_AU07_r, md_M_AU07_r, skew_M_AU07_r, kurtosis_M_AU07_r, min_M_AU09_r, max_M_AU09_r, sd_M_AU09_r, mean_M_AU09_r, md_M_AU09_r, skew_M_AU09_r, kurtosis_M_AU09_r, min_M_AU14_r, max_M_AU14_r, sd_M_AU14_r, mean_M_AU14_r, md_M_AU14_r, skew_M_AU14_r, kurtosis_M_AU14_r, min_M_AU15_r, max_M_AU15_r, sd_M_AU15_r, mean_M_AU15_r, md_M_AU15_r, skew_M_AU15_r, kurtosis_M_AU15_r, min_M_AU17_r, max_M_AU17_r, sd_M_AU17_r, mean_M_AU17_r, md_M_AU17_r, skew_M_AU17_r, kurtosis_M_AU17_r, min_M_AU20_r, max_M_AU20_r, sd_M_AU20_r, mean_M_AU20_r, md_M_AU20_r, skew_M_AU20_r, kurtosis_M_AU20_r, min_M_AU25_r, max_M_AU25_r, sd_M_AU25_r, mean_M_AU25_r, md_M_AU25_r, skew_M_AU25_r, kurtosis_M_AU25_r, min_M_AU26_r, max_M_AU26_r, sd_M_AU26_r, mean_M_AU26_r, md_M_AU26_r, skew_M_AU26_r, kurtosis_M_AU26_r, min_M_AU45_r, max_M_AU45_r, sd_M_AU45_r, mean_M_AU45_r, md_M_AU45_r, skew_M_AU45_r, kurtosis_M_AU45_r | 168 |
| HEADsync | min_M_headsync, max_M_headsync, sd_M_headsync, mean_M_headsync, md_M_headsync, skew_M_headsync, kurtosis_M_headsync, min_M_pose_Rxsync, max_M_pose_Rxsync, sd_M_pose_Rxsync, mean_M_pose_Rxsync, md_M_pose_Rxsync, skew_M_pose_Rxsync, kurtosis_M_pose_Rxsync, min_M_pose_Rysync, max_M_pose_Rysync, sd_M_pose_Rysync, mean_M_pose_Rysync, md_M_pose_Rysync, skew_M_pose_Rysync, kurtosis_M_pose_Rysync, min_M_pose_Rzsync, max_M_pose_Rzsync, sd_M_pose_Rzsync, mean_M_pose_Rzsync, md_M_pose_Rzsync, skew_M_pose_Rzsync, kurtosis_M_pose_Rzsync | 56 |
| INTRAsync | min_M_intra, max_M_intra, sd_M_intra, mean_M_intra, md_M_intra, skew_M_intra, kurtosis_M_intra | 14 |
| MovEx | M_body_total_movement, M_head_total_movement, mean_intensity_M | 6 |
| Speech | dyad_pit_sync_MEA_M_speech, dyad_int_sync_MEA_M_speech, dyad_spr_M_speech, dyad_str_M_speech, dyad_ttg_M_speech, dyad_no_turns_M_speech, nsyll_M_speech, npause_M_speech, pho_M_speech, art_M_speech, pit_sync_M_speech, int_sync_M_speech, art_sync_M_speech, pit_var_M_speech, int_var_M_speech | 30 |
While developing an algorithm for technology-assisted diagnostics of BPD was not the explicit goal of this research project, we explored the application of our features to the classification between BPD-involved and non-clinical interactions. Despite the features being chosen with symptoms and characteristics of ASD in mind, the CROSSturn, FACEsync, HEADsync and Speech models performed above chance in this comparison (BODYsync: pFDR = 1; CROSSsync: pFDR = 0.282; INTRAsync: pFDR = 1; MovEx: pFDR = 0.242). Specifically, the HEADsync model achieved 68.7% balanced accuracy (71.4; 65.9% specificity), the FACEsync 64% (64.3; 65.9% specificity), the Speech 61.6% (59.5; 63.6% specificity) and the CROSSturn 55.7% (52.4; 59.1% specificity). The stacking model performed comparable to the HEADsync and the MovEx model but outperformed the other base models (see [!T]), reaching 65.3% balanced accuracy (73.8; 56.8% specificity). Thus, the stacking model only misclassified eleven BPD-involved interactions as non-clinical, but 19 non-clinical interactions were labelled as BPD-involved.
| comparison | model | sens | spec | BAC | AUC | p.fdr | sig |
|---|---|---|---|---|---|---|---|
| ASD-COMP vs BPD-COMP | BODYsync | 32.353 | 47.619 | 39.986 | 0.361 | 1.000 | |
| ASD-COMP vs BPD-COMP | CROSSsync | 52.941 | 69.048 | 60.994 | 0.678 | 0.136 | |
| ASD-COMP vs BPD-COMP | CROSSturn | 67.647 | 85.714 | 76.681 | 0.749 | 0.000 | * |
| ASD-COMP vs BPD-COMP | FACEsync | 76.471 | 59.524 | 67.997 | 0.719 | 0.000 | * |
| ASD-COMP vs BPD-COMP | HEADsync | 55.882 | 57.143 | 56.513 | 0.609 | 0.936 | |
| ASD-COMP vs BPD-COMP | INTRAsync | 38.235 | 54.762 | 46.499 | 0.455 | 1.000 | |
| ASD-COMP vs BPD-COMP | MovEx | 58.824 | 78.571 | 68.698 | 0.758 | 0.027 | * |
| ASD-COMP vs BPD-COMP | Speech | 64.706 | 76.190 | 70.448 | 0.771 | 0.000 | * |
| ASD-COMP vs BPD-COMP | STACK | 70.588 | 92.857 | 81.723 | 0.805 | NaN | NA |
| ASD-COMP vs COMP-COMP | BODYsync | 58.824 | 61.364 | 60.094 | 0.607 | 0.000 | * |
| ASD-COMP vs COMP-COMP | CROSSsync | 67.647 | 75.000 | 71.324 | 0.763 | 0.000 | * |
| ASD-COMP vs COMP-COMP | CROSSturn | 44.118 | 72.727 | 58.422 | 0.627 | 0.000 | * |
| ASD-COMP vs COMP-COMP | FACEsync | 79.412 | 70.454 | 74.933 | 0.780 | 0.000 | * |
| ASD-COMP vs COMP-COMP | HEADsync | 58.824 | 68.182 | 63.503 | 0.608 | 1.000 | |
| ASD-COMP vs COMP-COMP | INTRAsync | 26.471 | 40.909 | 33.690 | 0.297 | 1.000 | |
| ASD-COMP vs COMP-COMP | MovEx | 64.706 | 72.727 | 68.717 | 0.777 | 0.000 | * |
| ASD-COMP vs COMP-COMP | Speech | 64.706 | 72.727 | 68.717 | 0.673 | 0.000 | * |
| ASD-COMP vs COMP-COMP | STACK | 79.412 | 81.818 | 80.615 | 0.845 | NaN | NA |
| BPD-COMP vs COMP-COMP | BODYsync | 28.571 | 36.364 | 32.468 | 0.308 | 1.000 | |
| BPD-COMP vs COMP-COMP | CROSSsync | 50.000 | 61.364 | 55.682 | 0.594 | 0.282 | |
| BPD-COMP vs COMP-COMP | CROSSturn | 52.381 | 59.091 | 55.736 | 0.569 | 0.045 | * |
| BPD-COMP vs COMP-COMP | FACEsync | 64.286 | 63.636 | 63.961 | 0.670 | 0.007 | * |
| BPD-COMP vs COMP-COMP | HEADsync | 71.429 | 65.909 | 68.669 | 0.736 | 0.000 | * |
| BPD-COMP vs COMP-COMP | INTRAsync | 57.143 | 54.545 | 55.844 | 0.516 | 1.000 | |
| BPD-COMP vs COMP-COMP | MovEx | 69.048 | 70.454 | 69.751 | 0.686 | 0.242 | |
| BPD-COMP vs COMP-COMP | Speech | 59.524 | 63.636 | 61.580 | 0.693 | 0.007 | * |
| BPD-COMP vs COMP-COMP | STACK | 73.810 | 56.818 | 65.314 | 0.760 | NaN | NA |
| comparison | model | BAC | p.fdr | sig |
|---|---|---|---|---|
| MultiGroup | BODYsync | 48.6 | 1.000 | |
| MultiGroup | CROSSsync | 58.1 | 0.000 | * |
| MultiGroup | CROSSturn | 57.3 | 0.000 | * |
| MultiGroup | FACEsync | 57.6 | 0.007 | * |
| MultiGroup | HEADsync | 54.6 | 0.000 | * |
| MultiGroup | INTRAsync | 49.5 | 1.000 | |
| MultiGroup | MovEx | 60.8 | 0.000 | * |
| MultiGroup | Speech | 63.9 | 0.000 | * |
| MultiGroup | STACK | 63.2 | NaN | NA |
Did the models perform better for one gender than the other? We perform unpaired Wilcoxon tests for the labels and models separately.
The following figures were created inspired by NeuroMiner15 visualisations and show the sign-based consistency16 as well as the feature weights of the models distinguishing between interaction partners from ASD- and BPD-involved interactions. For models with a large number of features, the top 16 features are plotted.